size <- 12. Later this will be the number of rows of the matrix.x <- rnorm( size ).x1 by adding (on average 10 times smaller) noise to x: x1 <- x + rnorm( size )/10.x and x1 should be close to 1.0: check this with function cor.x2 and x3 by adding (other) noise to x.size <- 12
x <- rnorm( size )
x1 <- x + rnorm( size )/10
cor( x, x1 )
[1] 0.990252
x2 <- x + rnorm( size )/10
x3 <- x + rnorm( size )/10
x1, x2 and x3 column-wise into a matrix using m <- cbind( x1, x2, x3 ).m.m.heatmap( m, Colv = NA, Rowv = NA, scale = "none" ).m <- cbind( x1, x2, x3 )
class( m )
[1] "matrix"
head( m )
x1 x2 x3
[1,] 0.49221962 0.49312398 0.4131327
[2,] -0.07519251 -0.03654146 -0.3084404
[3,] -0.21958797 -0.36474664 -0.2038947
[4,] -0.03465984 0.08680284 -0.2588878
[5,] 0.43147841 0.57490422 0.5855376
[6,] -0.68210268 -0.78841104 -0.8232175
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
# x1, x2, x3 follow similar color pattern, they should be correlated
y1…y4 (but not correlated with x), of the same length size.m from columns x1…x3,y1…y4 in some random order.y <- rnorm( size )
y1 <- y + rnorm( size )/10
y2 <- y + rnorm( size )/10
y3 <- y + rnorm( size )/10
y4 <- y + rnorm( size )/10
m <- cbind( y4, y3, x2, y1, x1, x3, y2 )
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
cc <- cor( m ) to build the matrix of correlation coefficients between columns of m.round( cc, 3 ) to show this matrix with 3 digits precision.cc <- cor( m )
round( cc, 3 ) #
y4 y3 x2 y1 x1 x3 y2
y4 1.000 0.990 -0.360 0.988 -0.339 -0.482 0.991
y3 0.990 1.000 -0.357 0.985 -0.332 -0.478 0.986
x2 -0.360 -0.357 1.000 -0.295 0.984 0.980 -0.402
y1 0.988 0.985 -0.295 1.000 -0.279 -0.426 0.989
x1 -0.339 -0.332 0.984 -0.279 1.000 0.980 -0.381
x3 -0.482 -0.478 0.980 -0.426 0.980 1.000 -0.517
y2 0.991 0.986 -0.402 0.989 -0.381 -0.517 1.000
heatmap( cc, symm = TRUE, scale = "none" )
# E.g. value for (row: x1, col: y1) is the corerlation of vectors x1, y1.
# Values of 1.0 are on the diagonal: e.g. x1 is perfectly correlated with x1.
# Correlations between x, x vectors are close to 1.0.
# Correlations between y, y vectors are close to 1.0.
# Correlations between x, y vectors are close to 0.0.
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